Sociology and the Problems of Problem Gambling Research: Connectin Private Troubles to Public Issues
Bibliographic record
Abstract
This study addresses the lack of sociological perspectives in theoretical frameworks commonly used in problem gambling research, and demonstrates the connection of often overlooked aspects of the social environment to variables commonly used to predict and explain problem gambling. Using the often studied correlates of problem gambling, namely,, anxiety disorders; mood disorders; and gender, each paper shows how the relationships between those correlates and problem gambling are significantly modified by features of the social environment. Contributions of sociological research to theoretical frameworks for explaining problem gambling are posited as modifications to the Pathways Model to Problem Gambling. Advanced generalized linear modeling is used to explore the interconnections of these relationships in all three studies. The research findings are discussed in relation to the dangers of reducing complex social issues such as the prevalence of problem gambling to a series of individual characteristics found in problem gamblers. Implications of governmental responsibility in gambling provision, the medicalization of abnormal behaviours, and the role of sociological research in identifying patterns of inequality are also explored.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.008 | 0.064 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".